Regulating Speech in Cyberspace: Gatekeepers, Human Rights, and Corporate Responsibility, by Emily B. Laidlaw
Bibliographic record
Abstract
Regulating speech in cyberspace is a daring, interdisciplinary work that meets at the intersection of free speech, corporate social responsibility, and human rights law. Written by Emily B. Laidlaw, an Assistant Professor at the University of Calgary, the book successfully carves out a unique space in the literature that is relatively unexplored. Although it situates itself in legal academia by offering an in-depth analysis of regulatory frameworks used to police online speech, the work draws heavily on communication theories, international development theories, and fundamental paradigms on human rights regimes. The result is a creative read that challenges us to re-conceptualize how online speech may be regulated by engaging with theories and concepts from a variety of disciplines. Laidlaw starts her book with the notion that privately owned companies control democratic societies’ pathways of communication. Her analysis brilliantly invokes the work of Jürgen Habermas to engage in a discussion of how online spaces may or may not resemble a form of the “public sphere.” One of the strengths of the book is this initial framework that Laidlaw creates. Using foundational communication theories, she helps the reader understand why technical, complex, and tedious regulatory frameworks for Internet gatekeepers matter for “deliberative” democracy. She argues that although the Internet and various online spaces are by no means perfectly analogous to Habermas’ public sphere, cafés and public squares of the past, such spaces do currently act as key sites for the exchange of ideas. This makes the Internet and online spaces critical for the flourishing of democracy. Regulating Speech in Cyberspace reminds the reader why debating and analyzing the fine minutia of corporate responsibility models and regulatory frameworks for online gatekeepers actually matters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".